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    Application of deep autoencoder model for structural condition monitoring

    271915.pdf (389.1Kb)
    Access Status
    Open access
    Authors
    Pathirage, C.
    Li, Jun
    Li, L.
    Hao, Hong
    Liu, Wan-Quan
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Pathirage, C. and Li, J. and Li, L. and Hao, H. and Liu, W. 2018. Application of deep autoencoder model for structural condition monitoring. Journal of Systems Engineering and Electronics. 29 (4): pp. 873-880.
    Source Title
    Journal of Systems Engineering and Electronics
    DOI
    10.21629/JSEE.2018.04.22
    ISSN
    1671-1793
    School
    School of Civil and Mechanical Engineering (CME)
    Remarks

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/71450
    Collection
    • Curtin Research Publications
    Abstract

    Damage detection in structures is performed via vibration based structural identification. Modal information, such as frequencies and mode shapes, are widely used for structural damage detection to indicate the health conditions of civil structures. The deep learning algorithm that works on a multiple layer neural network model termed as deep autoencoder is proposed to learn the relationship between the modal information and structural stiffness parameters. This is achieved via dimension reduction of the modal information feature and a non-linear regression against the structural stiffness parameters. Numerical tests on a symmetrical steel frame model are conducted to generate the data for the training and validation, and to demonstrate the efficiency of the proposed approach for vibration based structural damage detection.

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